Overview

Dataset statistics

Number of variables20
Number of observations706
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory110.4 KiB
Average record size in memory160.2 B

Variable types

Numeric10
Categorical9
Unsupported1

Alerts

age is highly correlated with sex and 16 other fieldsHigh correlation
sex is highly correlated with age and 12 other fieldsHigh correlation
cp is highly correlated with age and 16 other fieldsHigh correlation
trestbps is highly correlated with age and 15 other fieldsHigh correlation
htn is highly correlated with age and 14 other fieldsHigh correlation
chol is highly correlated with age and 16 other fieldsHigh correlation
fbs is highly correlated with age and 16 other fieldsHigh correlation
pro is highly correlated with age and 16 other fieldsHigh correlation
thalach is highly correlated with age and 16 other fieldsHigh correlation
thalrest is highly correlated with age and 16 other fieldsHigh correlation
tpeakbps is highly correlated with age and 16 other fieldsHigh correlation
tpeakbpd is highly correlated with age and 16 other fieldsHigh correlation
trestbpd is highly correlated with age and 15 other fieldsHigh correlation
exang is highly correlated with age and 16 other fieldsHigh correlation
xhypo is highly correlated with age and 16 other fieldsHigh correlation
oldpeak is highly correlated with age and 16 other fieldsHigh correlation
num is highly correlated with age and 16 other fieldsHigh correlation
restecg is highly correlated with df_index and 15 other fieldsHigh correlation
df_index is highly correlated with restecgHigh correlation
df_index has unique values Unique
dataset is an unsupported type, check if it needs cleaning or further analysis Unsupported
chol has 9 (1.3%) zeros Zeros
oldpeak has 215 (30.5%) zeros Zeros

Reproduction

Analysis started2022-10-17 05:21:58.414595
Analysis finished2022-10-17 05:22:07.137406
Duration8.72 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct706
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.4206799
Minimum0
Maximum899
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:07.193282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.5
Q1198.25
median389.5
Q3567.75
95-th percentile839.75
Maximum899
Range899
Interquartile range (IQR)369.5

Descriptive statistics

Standard deviation248.93543
Coefficient of variation (CV)0.614017593
Kurtosis-0.962561271
Mean405.4206799
Median Absolute Deviation (MAD)185
Skewness0.2469055446
Sum286227
Variance61968.84831
MonotonicityStrictly increasing
2022-10-17T07:22:07.280362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
5681
 
0.1%
5041
 
0.1%
5051
 
0.1%
5061
 
0.1%
5071
 
0.1%
5081
 
0.1%
5091
 
0.1%
5101
 
0.1%
5111
 
0.1%
Other values (696)696
98.6%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
8991
0.1%
8971
0.1%
8951
0.1%
8941
0.1%
8931
0.1%
8921
0.1%
8911
0.1%
8901
0.1%
8891
0.1%
8881
0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct48
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.10623229
Minimum1
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:07.367440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q351
95-th percentile63
Maximum76
Range75
Interquartile range (IQR)50

Descriptive statistics

Standard deviation25.54670407
Coefficient of variation (CV)1.01754432
Kurtosis-1.70644901
Mean25.10623229
Median Absolute Deviation (MAD)0
Skewness0.2422316204
Sum17725
Variance652.6340888
MonotonicityNot monotonic
2022-10-17T07:22:07.449301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1361
51.1%
5423
 
3.3%
5117
 
2.4%
5216
 
2.3%
4116
 
2.3%
4214
 
2.0%
5513
 
1.8%
4413
 
1.8%
5312
 
1.7%
5712
 
1.7%
Other values (38)209
29.6%
ValueCountFrequency (%)
1361
51.1%
281
 
0.1%
292
 
0.3%
301
 
0.1%
311
 
0.1%
323
 
0.4%
331
 
0.1%
345
 
0.7%
355
 
0.7%
364
 
0.6%
ValueCountFrequency (%)
761
 
0.1%
751
 
0.1%
741
 
0.1%
713
0.4%
701
 
0.1%
692
 
0.3%
683
0.4%
673
0.4%
666
0.8%
654
0.6%

sex
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
577 
0.0
129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0577
81.7%
0.0129
 
18.3%

Length

2022-10-17T07:22:07.525564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:07.591383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0577
81.7%
0.0129
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0835
39.4%
.706
33.3%
1577
27.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0835
59.1%
1577
40.9%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0835
39.4%
.706
33.3%
1577
27.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0835
39.4%
.706
33.3%
1577
27.2%

cp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
384 
2.0
121 
3.0
109 
4.0
92 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0384
54.4%
2.0121
 
17.1%
3.0109
 
15.4%
4.092
 
13.0%

Length

2022-10-17T07:22:07.647409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:07.717700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0384
54.4%
2.0121
 
17.1%
3.0109
 
15.4%
4.092
 
13.0%

Most occurring characters

ValueCountFrequency (%)
.706
33.3%
0706
33.3%
1384
18.1%
2121
 
5.7%
3109
 
5.1%
492
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0706
50.0%
1384
27.2%
2121
 
8.6%
3109
 
7.7%
492
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.706
33.3%
0706
33.3%
1384
18.1%
2121
 
5.7%
3109
 
5.1%
492
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.706
33.3%
0706
33.3%
1384
18.1%
2121
 
5.7%
3109
 
5.1%
492
 
4.3%

trestbps
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.96600567
Minimum1
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:07.787415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3130
95-th percentile150
Maximum190
Range189
Interquartile range (IQR)129

Descriptive statistics

Standard deviation65.50782731
Coefficient of variation (CV)1.024103766
Kurtosis-1.841145132
Mean63.96600567
Median Absolute Deviation (MAD)0
Skewness0.1453953077
Sum45160
Variance4291.275438
MonotonicityNot monotonic
2022-10-17T07:22:07.866340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1361
51.1%
12069
 
9.8%
13058
 
8.2%
14043
 
6.1%
11023
 
3.3%
15020
 
2.8%
16015
 
2.1%
1259
 
1.3%
1388
 
1.1%
1127
 
1.0%
Other values (34)93
 
13.2%
ValueCountFrequency (%)
1361
51.1%
942
 
0.3%
981
 
0.1%
1006
 
0.8%
1011
 
0.1%
1022
 
0.3%
1042
 
0.3%
1054
 
0.6%
1062
 
0.3%
1085
 
0.7%
ValueCountFrequency (%)
1901
 
0.1%
1805
 
0.7%
1781
 
0.1%
1721
 
0.1%
1703
 
0.4%
16015
2.1%
1561
 
0.1%
1551
 
0.1%
1541
 
0.1%
1523
 
0.4%

htn
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
517 
0.0
189 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0517
73.2%
0.0189
 
26.8%

Length

2022-10-17T07:22:07.942215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:08.008188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0517
73.2%
0.0189
 
26.8%

Most occurring characters

ValueCountFrequency (%)
0895
42.3%
.706
33.3%
1517
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0895
63.4%
1517
36.6%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0895
42.3%
.706
33.3%
1517
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0895
42.3%
.706
33.3%
1517
24.4%

chol
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct160
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.0750708
Minimum0
Maximum564
Zeros9
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:08.073601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q3230
95-th percentile305.75
Maximum564
Range564
Interquartile range (IQR)229

Descriptive statistics

Standard deviation125.5176347
Coefficient of variation (CV)1.090745666
Kurtosis-1.336156365
Mean115.0750708
Median Absolute Deviation (MAD)0
Skewness0.3944083392
Sum81243
Variance15754.67663
MonotonicityNot monotonic
2022-10-17T07:22:08.153051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361
51.1%
09
 
1.3%
2207
 
1.0%
2117
 
1.0%
2406
 
0.8%
2235
 
0.7%
2505
 
0.7%
1975
 
0.7%
1965
 
0.7%
2045
 
0.7%
Other values (150)291
41.2%
ValueCountFrequency (%)
09
 
1.3%
1361
51.1%
851
 
0.1%
1001
 
0.1%
1261
 
0.1%
1291
 
0.1%
1321
 
0.1%
1411
 
0.1%
1472
 
0.3%
1491
 
0.1%
ValueCountFrequency (%)
5641
0.1%
4581
0.1%
4171
0.1%
4121
0.1%
3942
0.3%
3651
0.1%
3601
0.1%
3581
0.1%
3541
0.1%
3471
0.1%

fbs
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
398 
0.0
308 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0398
56.4%
0.0308
43.6%

Length

2022-10-17T07:22:08.231262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:08.295116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0398
56.4%
0.0308
43.6%

Most occurring characters

ValueCountFrequency (%)
01014
47.9%
.706
33.3%
1398
 
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01014
71.8%
1398
 
28.2%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01014
47.9%
.706
33.3%
1398
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01014
47.9%
.706
33.3%
1398
 
18.8%

restecg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
404 
0.0
225 
2.0
77 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0404
57.2%
0.0225
31.9%
2.077
 
10.9%

Length

2022-10-17T07:22:08.350357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:08.417542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0404
57.2%
0.0225
31.9%
2.077
 
10.9%

Most occurring characters

ValueCountFrequency (%)
0931
44.0%
.706
33.3%
1404
19.1%
277
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0931
65.9%
1404
28.6%
277
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0931
44.0%
.706
33.3%
1404
19.1%
277
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0931
44.0%
.706
33.3%
1404
19.1%
277
 
3.6%

pro
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
390 
0.0
316 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0390
55.2%
0.0316
44.8%

Length

2022-10-17T07:22:08.476530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:08.542142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0390
55.2%
0.0316
44.8%

Most occurring characters

ValueCountFrequency (%)
01022
48.3%
.706
33.3%
1390
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01022
72.4%
1390
 
27.6%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01022
48.3%
.706
33.3%
1390
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01022
48.3%
.706
33.3%
1390
 
18.4%

thalach
Real number (ℝ≥0)

HIGH CORRELATION

Distinct88
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.44759207
Minimum1
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:08.610112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3151
95-th percentile178
Maximum202
Range201
Interquartile range (IQR)150

Descriptive statistics

Standard deviation75.98186197
Coefficient of variation (CV)1.034504465
Kurtosis-1.832545843
Mean73.44759207
Median Absolute Deviation (MAD)0
Skewness0.1757378525
Sum51854
Variance5773.243349
MonotonicityNot monotonic
2022-10-17T07:22:08.695320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361
51.1%
15019
 
2.7%
16016
 
2.3%
14014
 
2.0%
17013
 
1.8%
17210
 
1.4%
1629
 
1.3%
1429
 
1.3%
1209
 
1.3%
1528
 
1.1%
Other values (78)238
33.7%
ValueCountFrequency (%)
1361
51.1%
691
 
0.1%
801
 
0.1%
862
 
0.3%
901
 
0.1%
963
 
0.4%
971
 
0.1%
982
 
0.3%
991
 
0.1%
1005
 
0.7%
ValueCountFrequency (%)
2021
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.3%
1881
 
0.1%
1871
 
0.1%
1862
0.3%
1854
0.6%
1844
0.6%
1823
0.4%

thalrest
Real number (ℝ≥0)

HIGH CORRELATION

Distinct66
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.90509915
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:08.783513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q376.75
95-th percentile98
Maximum134
Range133
Interquartile range (IQR)75.75

Descriptive statistics

Standard deviation40.30332935
Coefficient of variation (CV)1.03593951
Kurtosis-1.615820873
Mean38.90509915
Median Absolute Deviation (MAD)0
Skewness0.273178687
Sum27467
Variance1624.358357
MonotonicityNot monotonic
2022-10-17T07:22:08.871943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361
51.1%
8017
 
2.4%
8414
 
2.0%
7312
 
1.7%
7512
 
1.7%
9011
 
1.6%
7411
 
1.6%
7811
 
1.6%
9811
 
1.6%
7611
 
1.6%
Other values (56)235
33.3%
ValueCountFrequency (%)
1361
51.1%
461
 
0.1%
492
 
0.3%
502
 
0.3%
511
 
0.1%
524
 
0.6%
532
 
0.3%
544
 
0.6%
551
 
0.1%
563
 
0.4%
ValueCountFrequency (%)
1341
 
0.1%
1253
0.4%
1241
 
0.1%
1202
0.3%
1191
 
0.1%
1161
 
0.1%
1151
 
0.1%
1122
0.3%
1103
0.4%
1052
0.3%

tpeakbps
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.00283286
Minimum1
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:08.957596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q3172
95-th percentile200
Maximum240
Range239
Interquartile range (IQR)171

Descriptive statistics

Standard deviation88.70905504
Coefficient of variation (CV)1.031466663
Kurtosis-1.831060575
Mean86.00283286
Median Absolute Deviation (MAD)0
Skewness0.1589203153
Sum60718
Variance7869.296446
MonotonicityNot monotonic
2022-10-17T07:22:09.257420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1361
51.1%
18043
 
6.1%
16036
 
5.1%
17032
 
4.5%
19031
 
4.4%
20028
 
4.0%
14016
 
2.3%
15016
 
2.3%
14510
 
1.4%
22010
 
1.4%
Other values (47)123
 
17.4%
ValueCountFrequency (%)
1361
51.1%
841
 
0.1%
1121
 
0.1%
1161
 
0.1%
1202
 
0.3%
1242
 
0.3%
1301
 
0.1%
1341
 
0.1%
1352
 
0.3%
1361
 
0.1%
ValueCountFrequency (%)
2404
 
0.6%
2351
 
0.1%
2321
 
0.1%
2306
0.8%
2281
 
0.1%
2241
 
0.1%
22010
1.4%
2108
1.1%
2081
 
0.1%
2041
 
0.1%

tpeakbpd
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.95325779
Minimum1
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:09.341310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q387.5
95-th percentile105.75
Maximum134
Range133
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation44.27828595
Coefficient of variation (CV)1.030848141
Kurtosis-1.759452809
Mean42.95325779
Median Absolute Deviation (MAD)0
Skewness0.2138583281
Sum30325
Variance1960.566606
MonotonicityNot monotonic
2022-10-17T07:22:09.430720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1361
51.1%
8062
 
8.8%
9053
 
7.5%
10046
 
6.5%
7025
 
3.5%
11022
 
3.1%
6013
 
1.8%
7811
 
1.6%
958
 
1.1%
857
 
1.0%
Other values (32)98
 
13.9%
ValueCountFrequency (%)
1361
51.1%
261
 
0.1%
402
 
0.3%
451
 
0.1%
501
 
0.1%
561
 
0.1%
583
 
0.4%
6013
 
1.8%
622
 
0.3%
641
 
0.1%
ValueCountFrequency (%)
1341
 
0.1%
1206
 
0.8%
1182
 
0.3%
1161
 
0.1%
1152
 
0.3%
11022
3.1%
1081
 
0.1%
1061
 
0.1%
1053
 
0.4%
1047
 
1.0%

trestbpd
Real number (ℝ≥0)

HIGH CORRELATION

Distinct30
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.35552408
Minimum1
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2022-10-17T07:22:09.516575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q380
95-th percentile98
Maximum110
Range109
Interquartile range (IQR)79

Descriptive statistics

Standard deviation41.87820509
Coefficient of variation (CV)1.012638723
Kurtosis-1.885576639
Mean41.35552408
Median Absolute Deviation (MAD)0
Skewness0.12575732
Sum29197
Variance1753.784062
MonotonicityNot monotonic
2022-10-17T07:22:09.593345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1361
51.1%
80103
 
14.6%
9070
 
9.9%
7038
 
5.4%
10028
 
4.0%
8512
 
1.7%
7811
 
1.6%
848
 
1.1%
867
 
1.0%
727
 
1.0%
Other values (20)61
 
8.6%
ValueCountFrequency (%)
1361
51.1%
501
 
0.1%
604
 
0.6%
643
 
0.4%
661
 
0.1%
682
 
0.3%
7038
 
5.4%
727
 
1.0%
746
 
0.8%
754
 
0.6%
ValueCountFrequency (%)
1102
 
0.3%
1061
 
0.1%
1052
 
0.3%
1041
 
0.1%
1021
 
0.1%
10028
4.0%
986
 
0.8%
964
 
0.6%
957
 
1.0%
945
 
0.7%

exang
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
413 
0.0
293 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0413
58.5%
0.0293
41.5%

Length

2022-10-17T07:22:09.668610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:09.735036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0413
58.5%
0.0293
41.5%

Most occurring characters

ValueCountFrequency (%)
0999
47.2%
.706
33.3%
1413
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0999
70.8%
1413
29.2%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0999
47.2%
.706
33.3%
1413
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0999
47.2%
.706
33.3%
1413
19.5%

xhypo
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
362 
0.0
344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2118
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0362
51.3%
0.0344
48.7%

Length

2022-10-17T07:22:09.793369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:09.860180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0362
51.3%
0.0344
48.7%

Most occurring characters

ValueCountFrequency (%)
01050
49.6%
.706
33.3%
1362
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1412
66.7%
Other Punctuation706
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01050
74.4%
1362
 
25.6%
Other Punctuation
ValueCountFrequency (%)
.706
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01050
49.6%
.706
33.3%
1362
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01050
49.6%
.706
33.3%
1362
 
17.1%

oldpeak
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7205382436
Minimum-0.5
Maximum4.2
Zeros215
Zeros (%)30.5%
Negative1
Negative (%)0.1%
Memory size5.6 KiB
2022-10-17T07:22:09.919238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile0
Q10
median1
Q31
95-th percentile1.5
Maximum4.2
Range4.7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.577021203
Coefficient of variation (CV)0.8008196763
Kurtosis2.517200266
Mean0.7205382436
Median Absolute Deviation (MAD)0
Skewness0.6098833987
Sum508.7
Variance0.3329534687
MonotonicityNot monotonic
2022-10-17T07:22:09.989919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1384
54.4%
0215
30.5%
0.610
 
1.4%
1.510
 
1.4%
210
 
1.4%
0.29
 
1.3%
0.88
 
1.1%
0.47
 
1.0%
1.67
 
1.0%
0.57
 
1.0%
Other values (17)39
 
5.5%
ValueCountFrequency (%)
-0.51
 
0.1%
0215
30.5%
0.14
 
0.6%
0.29
 
1.3%
0.32
 
0.3%
0.47
 
1.0%
0.57
 
1.0%
0.610
 
1.4%
0.71
 
0.1%
0.88
 
1.1%
ValueCountFrequency (%)
4.21
 
0.1%
3.51
 
0.1%
33
 
0.4%
2.61
 
0.1%
2.41
 
0.1%
2.32
 
0.3%
210
1.4%
1.92
 
0.3%
1.83
 
0.4%
1.67
1.0%

num
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
361 
0
345 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters706
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1361
51.1%
0345
48.9%

Length

2022-10-17T07:22:10.059277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:22:10.124643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1361
51.1%
0345
48.9%

Most occurring characters

ValueCountFrequency (%)
1361
51.1%
0345
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number706
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1361
51.1%
0345
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1361
51.1%
0345
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1361
51.1%
0345
48.9%

dataset
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size5.6 KiB

Interactions

2022-10-17T07:22:05.902474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.039074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.926785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.744652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.479646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.176732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.097060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.812398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.503652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.221797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.178896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.196865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.000330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.815989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.544267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.246405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.168922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.878175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.571048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.287178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.247048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.287908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.098458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.891784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.616899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.323692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.241662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.950182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.645370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.357466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.314687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.373768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.183625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.966362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.686531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.397935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.314091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.022382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.718186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.427779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.378896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.446151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.265795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.040318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.761783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.662646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.383553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.089606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.790039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.493334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.451936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.534188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.364789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.121310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.841136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.740111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.459381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.162390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.864519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.565017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.524342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.621036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.447078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.196947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.913236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.813882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.534280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.232648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.939515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.634321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.595785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.694122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.525275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.265532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.978864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.883315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.604279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.298905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.010647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.701755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.669686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.775600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.605887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.338811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.047925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.957202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.677304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.369101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.083428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.773028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:06.736505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:59.852021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:00.677390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:01.412171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:02.112250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.028754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:03.744863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:04.436906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.152544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:22:05.838048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-17T07:22:10.188332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-17T07:22:10.323984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-17T07:22:10.453598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-17T07:22:10.572151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-17T07:22:10.670324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-17T07:22:06.861709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-17T07:22:07.070879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagesexcptrestbpshtncholfbsrestecgprothalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaknumdataset
0040.01.02.0140.00.0289.00.00.00.0172.086.0200.0110.086.00.00.00.00hungarian
111.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
2237.01.02.0130.00.0283.00.01.00.098.058.0180.0100.080.00.00.00.00hungarian
331.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
4539.01.03.0120.00.0339.00.00.00.0170.086.0198.0100.080.00.00.00.00hungarian
5645.00.02.0130.00.0237.00.00.00.0170.090.0200.0106.084.00.00.00.00hungarian
6754.01.02.0110.00.0208.00.00.00.0142.056.0220.070.070.00.00.00.00hungarian
781.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
8948.00.02.0120.00.0284.00.00.00.0120.072.0140.080.080.00.00.00.00hungarian
91037.00.03.0130.00.0211.00.00.00.0142.057.0180.0100.080.00.00.00.00hungarian

Last rows

df_indexagesexcptrestbpshtncholfbsrestecgprothalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeaknumdataset
6968881.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
69788968.01.03.0134.01.0254.01.00.00.0151.063.0174.0110.080.01.00.00.00long-beach-va
69889051.00.04.0114.01.0258.01.02.00.096.052.0140.096.074.00.00.01.00long-beach-va
6998911.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7008921.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7018931.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7028941.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7038951.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7048971.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011
7058991.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.01.011